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Assessment of SPOT 5 and ERS-2 OBIA for mapping wetlands

Pauw, Theo (2012-12)

Thesis (MSc)--Stellenbosch University, 2012.

Thesis

ENGLISH ABSTRACT: This research considered the automated remote sensing-based classification of wetland extent within the Nuwejaars and Heuningnes River systems on the Agulhas Plain. The classification process was based on meaningful image objects created through image segmentation rather than on single pixels. An expert system classifier was compared to a nearest-neighbour supervised classifier, and one multispectral (SPOT 5) image (dry season) and two C-band, VV-polarisation synthetic aperture radar (SAR: ERS-2) images (dry and wet season) were used separately and in combination.
Classifications were performed within two subset areas. Final classes identified were Permanent waterbody, Other wetland and Non-wetland. Statistical accuracy assessment was performed. Validation data was derived from a combination of high-resolution aerial photographs, the SPOT 5 image, high-resolution imagery on Google Earth and observations during a field visit. Wetland extent was defined as the total extent of wetland-specific vegetation, unvegetated seasonal pans and waterbodies. More detailed classes were originally envisaged, but available validation data was not considered adequate for assessing their accuracy with any confidence.
The supervised classifier was found to be more accurate overall than the developed expert system. The difference between the two was however not always significant. The two SAR images alone did not contain sufficient information for the accurate classification of Agulhas wetlands’ extent, with recorded overall accuracies not exceeding 65% regardless of the classifier used. The SPOT image alone achieved accuracies higher than 80%; this was considered a good result. In comparison, combining the SAR and SPOT data did not improve the classification accuracy.
The potential of the expert system to be applied with little modification to images acquired over other areas or over the same area in other years should be further investigated. However, several reservations are noted in this regard. Future research could potentially improve the results obtained from supervised classification by augmenting it with expert system rules to identify more complicated classes.
KEYWORDS
ERS-2, SPOT 5, SAR, wetlands, expert system classifier, nearest-neighbour supervised classifier